Harvard partnered with SYSTRAN to develop the next generation of AI-based Language Translation that runs on Neural Networks. They call it Pure Neural Machine Translation (PNMT). SYSTRAN will sponsor FIBA AML to showcase how PNMT is helping companies solve the linguistic challenges related to multinational compliance and AML.

“PNMT is giving the banking industry a more powerful tool to maintain compliance, even when working with terabytes of data stored in multiple languages around the world,” says Ken Behan, Vice President of Sales and Marketing of SYSTRAN.

The PNMT engine is revolutionary in that it processes an entire sentence or paragraph in the context of the overall document topic, instead of translating segment by segment. This creates a far more accurate output than ever before seen with machine translation, especially for Asian languages. For compliance, accuracy in translation is important in being able to flag issues or suspicious transactions.

SYSTRAN’s software provides the ability to perform machine translation on both audio and text in 45+ language pairs.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept and how it can be utilized by compliance teams. For more information, contact Craig Stern at craig.stern@systrangroup.com and to set up a meeting, click here.

SYSTRAN, a global leader in language translation technology, will showcase its newest translation software, called Pure Neural Machine Translation (PNMT), at Legaltech in New York this month.

As the demand for multi-lingual litigation continues to increase, law firms need a way to translate eDiscovery data quickly, reliably and cost-effectively. PNMT is the perfect solution to this challenge.

“PNMT offers an incredible opportunity for legal firms to perform multi-lingual eDiscovery more efficiently,” says Ken Behan, Vice President of Sales and Marketing of SYSTRAN. “Having the ability to automatically translate terabytes of data and get reliable results is invaluable for law firms, especially when timelines and resources are tight.”

The PNMT engine is revolutionary in that it processes an entire sentence or paragraph in the context of the overall document topic, instead of translating segment by segment. This creates a far more accurate output than ever before seen with machine translation, especially for Asian languages.

In fact, early tests show that PNMT translated documents are of the same or even higher quality than human-translated content. Test subjects could not correctly identify which translated samples were done by machine translation versus a human. The quality is that good.

SYSTRAN’s software provides legal organizations the ability to perform eDiscovery translation on both audio and text in real-time in 45+ language pairs. The PNMT software can be used as a connector to eDiscovery software, such as Relativity, or on its own.

SYSTRAN’s team is setting private meetings for an exclusive view of the PNMT concept and how it can be utilized by legal teams to boost productivity and cut translation costs during eDiscovery. To set up a meeting or schedule a demo during Legaltech, contact Craig Stern at craig.stern@systrangroup.com

When a global enterprise gets sued, it’s vital to know who is involved and how. But finding out who to blame isn’t always simple.

Global law firms are tasked with sifting through thousands, sometimes millions of emails, chats, and legal documentation during eDiscovery. These documents and audio recordings could be in many different languages and stored around the world. Sometimes that data is stored in countries with strong data protection regulations, such as Brazil and parts of the EU, so it cannot under any circumstances leave the country.

So, how can an office in the U.S. review hundreds of days of correspondence in multiple languages?

If the firm hires translators, they’ll need dozens with a strong knowledge of everything from slang to deep subject matter expertise of the topic in discovery. If instead they decide to go with an e-discovery translation solution, they’ll still need help during the review process, especially for data in Asian languages – there are several ways to interpret one word, for which there may be five slang alternatives. In either case, the team must spend a lot of time and money to get reliable and accurate results.

by Kirti Vashee on eMpTy Pages, a blog about translation technology, localization and collaboration

Recently, I had the opportunity and kind invitation to attend the SYSTRAN community day event where many members of their product development, marketing, and management team gathered with major customers and partners.

The objective was to share information about the continuing evolution of their new Pure Neural MT (PNMT) technology, share detailed PNMT output quality evaluation results, and provide initial customer user experience data with the new technology. Also, naturally such an event creates a more active and intense dialogue between company employees and customers and partners. This, I think has substantial value for a company that seeks to align product offerings with its customer’s actual needs.

Ongoing Enhancements of the PNMT Product Offering

The event made it clear that SYSTRAN is well down the NMT path, possibly years ahead of other MT vendors, and provided a review of the current status of their rapidly evolving PNMT technology.

Round-trip translation (RTT), also known as back-and-forth translation, recursive translation and bi-directional translation, is the process of translating a word, phrase or text into another language (forward translation), then translating the result back into the original language (back translation), using machine translation (MT) software.
It is often used by laypeople to evaluate a machine translation system, or to test whether a text is suitable for MT when they are unfamiliar with the target language. Because the resulting text can often differ substantially from the original, RTT can also be a source of entertainment*.

Yesterday I translated our company presentation with Systran’s new Pure Neural™ Machine Translation (PNMT™) engine, and I was amazed at the results.

The presentation in question was a complete overview of all of our services, 59 pages of French text that was edited three separate times to make sure the quality was perfect. (Thanks Faten, Boris and Laurence!)

Then, two days ago, just as I was putting the finishing touches on the presentation for a response to an RFP (Request For Proposals), I found out that our prospective client (a major French manufacturer) wanted our response in English. I had just one day to deliver 59 pages of perfect English content!

Let me give you some background to explain why I, the CEO of a translation company, decided to use Neural Machine Translation for one of our most important commercial documents for one of our most important tenders.

We are SYSTRAN. We love languages, lots of languages. We are a human-sized company but we have linguists for almost all of the 140 language pairs we support. That’s a big number, but don’t be misled- some of us are fluent in many languages. Nevertheless, we love languages and we don’t believe in the one-fits-all technology regarding language processing.

The results obtained from Neural Machine Translation are amazing, in particular, the neural network’s paraphrasing. It almost seems as if the neural network really “understands” the sentence to translate. In this first article, we are interested in “meaning,” that which gives an idea of the type of semantic knowledge the neural networks use to translate.

Let us start with a glimpse of how the 3 technologies work, the different steps of each translation process and the resources that each technology uses to translate. Then we will take a look at a few examples and compare what each technology must do to translate them correctly.

Project “PNMT” for Purely Neural Machine Translation was this year’s flagship project for the researchers and developers at SYSTRAN.

SYSTRAN brings its expertise in several ways: contributing to research on neural models; applying its know-how in terminology to increase the potential of Neural Machine Translation; and industrializing technology to make it available to companies, organizations and individuals.
We will keep you posted each month and share best practices, research paper, customers insights, product news…